sywang/TPIPS-Embed-Qwen3VL-8B

TPIPS — Embedding (late fusion)

Text-conditioned perceptual image similarity, built on Qwen/Qwen3-VL-Embedding-8B. This repo holds the `embedding` checkpoint (one of three TPIPS models, each in its own repo — see the table at the bottom). Code and full docs: https://github.com/adobe-research/TPIPS.

Late fusion. Each (text, image) is encoded independently into an L2-normalised embedding. The pairwise score is cos(e_a, e_b) (higher = more similar). Odd-one-out probabilities are a softmax over the three "other-pair" scores divided by the temperature; 2AFC compares the two reference-candidate scores. The pairwise score is the model's raw output (temperature is applied at the probability step).

Usage

TPIPS supports Python 3.10 and later. Install matching PyTorch and torchvision builds from the official PyTorch installer, then install TPIPS:

pip install tpips

Start with the recommended embedding model:

import tpips
from PIL import Image

model = tpips.load_model("embedding", device="cuda")
a = Image.open("a.jpg").convert("RGB")
b = Image.open("b.jpg").convert("RGB")

similarity = model.similarity(a, b, factor="lighting")  # higher is more similar
distance = model.distance(a, b, factor="lighting")      # lower is more similar

The first call downloads the selected TPIPS checkpoint and its Qwen backbone. A CUDA GPU is recommended; FlashAttention is optional.

The TPIPS models

License

TPIPS is provided under the Adobe Research License for noncommercial research use. See the license for the complete terms.